Not all retrievers use `k` as param name to set the number of results to
return. Even in LangChain itself. Eg:
bc4251b9e0/libs/core/langchain_core/indexing/in_memory.py (L31)
So it's helpful to be able to change it for a given retriever.
The change also adds hints to disable the tests if the retriever doesn't
support setting the param in the constructor or in the invoke method
(for instance, the `InMemoryDocumentIndex` in the link supports in the
constructor but not in the invoke method).
This change is backward compatible.
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
**Description:** fix an issue I discovered when attempting to merge
messages in which one message has an `index` key in its content
dictionary and another does not.
langchain-gradientai is Digitalocean's integration with Langchain. It
will help users to build langchain applications using Digitalocean's
GradientAI platform.
---------
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Fixes a streaming bug where models like Qwen3 (using OpenAI interface)
send tool call chunks with inconsistent indices, resulting in
duplicate/erroneous tool calls instead of a single merged tool call.
## Problem
When Qwen3 streams tool calls, it sends chunks with inconsistent `index`
values:
- First chunk: `index=1` with tool name and partial arguments
- Subsequent chunks: `index=0` with `name=None`, `id=None` and argument
continuation
The existing `merge_lists` function only merges chunks when their
`index` values match exactly, causing these logically related chunks to
remain separate, resulting in multiple incomplete tool calls instead of
one complete tool call.
```python
# Before fix: Results in 1 valid + 1 invalid tool call
chunk1 = AIMessageChunk(tool_call_chunks=[
{"name": "search", "args": '{"query":', "id": "call_123", "index": 1}
])
chunk2 = AIMessageChunk(tool_call_chunks=[
{"name": None, "args": ' "test"}', "id": None, "index": 0}
])
merged = chunk1 + chunk2 # Creates 2 separate tool calls
# After fix: Results in 1 complete tool call
merged = chunk1 + chunk2 # Creates 1 merged tool call: search({"query": "test"})
```
## Solution
Enhanced the `merge_lists` function in `langchain_core/utils/_merge.py`
with intelligent tool call chunk merging:
1. **Preserves existing behavior**: Same-index chunks still merge as
before
2. **Adds special handling**: Tool call chunks with
`name=None`/`id=None` that don't match any existing index are now merged
with the most recent complete tool call chunk
3. **Maintains backward compatibility**: All existing functionality
works unchanged
4. **Targeted fix**: Only affects tool call chunks, doesn't change
behavior for other list items
The fix specifically handles the pattern where:
- A continuation chunk has `name=None` and `id=None` (indicating it's
part of an ongoing tool call)
- No matching index is found in existing chunks
- There exists a recent tool call chunk with a valid name or ID to merge
with
## Testing
Added comprehensive test coverage including:
- ✅ Qwen3-style chunks with different indices now merge correctly
- ✅ Existing same-index behavior preserved
- ✅ Multiple distinct tool calls remain separate
- ✅ Edge cases handled (empty chunks, orphaned continuations)
- ✅ Backward compatibility maintained
Fixes#31511.
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Co-authored-by: mdrxy <61371264+mdrxy@users.noreply.github.com>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Co-authored-by: Mason Daugherty <mason@langchain.dev>
## Problem
ChatLiteLLM encounters a `ValidationError` when using cache on
subsequent calls, causing the following error:
```
ValidationError(model='ChatResult', errors=[{'loc': ('generations', 0, 'type'), 'msg': "unexpected value; permitted: 'ChatGeneration'", 'type': 'value_error.const', 'ctx': {'given': 'Generation', 'permitted': ('ChatGeneration',)}}])
```
This occurs because:
1. The cache stores `Generation` objects (with `type="Generation"`)
2. But `ChatResult` expects `ChatGeneration` objects (with
`type="ChatGeneration"` and a required `message` field)
3. When cached values are retrieved, validation fails due to the type
mismatch
## Solution
Added graceful handling in both sync (`_generate_with_cache`) and async
(`_agenerate_with_cache`) cache methods to:
1. **Detect** when cached values contain `Generation` objects instead of
expected `ChatGeneration` objects
2. **Convert** them to `ChatGeneration` objects by wrapping the text
content in an `AIMessage`
3. **Preserve** all original metadata (`generation_info`)
4. **Allow** `ChatResult` creation to succeed without validation errors
## Example
```python
# Before: This would fail with ValidationError
from langchain_community.chat_models import ChatLiteLLM
from langchain_community.cache import SQLiteCache
from langchain.globals import set_llm_cache
set_llm_cache(SQLiteCache(database_path="cache.db"))
llm = ChatLiteLLM(model_name="openai/gpt-4o", cache=True, temperature=0)
print(llm.predict("test")) # Works fine (cache empty)
print(llm.predict("test")) # Now works instead of ValidationError
# After: Seamlessly handles both Generation and ChatGeneration objects
```
## Changes
- **`libs/core/langchain_core/language_models/chat_models.py`**:
- Added `Generation` import from `langchain_core.outputs`
- Enhanced cache retrieval logic in `_generate_with_cache` and
`_agenerate_with_cache` methods
- Added conversion from `Generation` to `ChatGeneration` objects when
needed
-
**`libs/core/tests/unit_tests/language_models/chat_models/test_cache.py`**:
- Added test case to validate the conversion logic handles mixed object
types
## Impact
- **Backward Compatible**: Existing code continues to work unchanged
- **Minimal Change**: Only affects cache retrieval path, no API changes
- **Robust**: Handles both legacy cached `Generation` objects and new
`ChatGeneration` objects
- **Preserves Data**: All original content and metadata is maintained
during conversion
Fixes#22389.
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Co-authored-by: mdrxy <61371264+mdrxy@users.noreply.github.com>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
**Description:** Fixes incorrect `num_skipped` count in the LangChain
indexing API. The current implementation only counts documents that
already exist in RecordManager (cross-batch duplicates) but fails to
count documents removed during within-batch deduplication via
`_deduplicate_in_order()`.
This PR adds tracking of the original batch size before deduplication
and includes the difference in `num_skipped`, ensuring that `num_added +
num_skipped` equals the total number of input documents.
**Issue:** Fixes incorrect document count reporting in indexing
statistics
**Dependencies:** None
Fixes#32272
---------
Co-authored-by: Alex Feel <afilippov@spotware.com>
Ensures proper reStructuredText formatting by adding the required blank
line before closing docstring quotes, which resolves the "Block quote
ends without a blank line; unexpected unindent" warning.
**TL;DR much of the provided `Makefile` targets were broken, and any
time I wanted to preview changes locally I either had to refer to a
command Chester gave me or try waiting on a Vercel preview deployment.
With this PR, everything should behave like normal.**
Significant updates to the `Makefile` and documentation files, focusing
on improving usability, adding clear messaging, and fixing/enhancing
documentation workflows.
### Updates to `Makefile`:
#### Enhanced build and cleaning processes:
- Added informative messages (e.g., "📚 Building LangChain
documentation...") to makefile targets like `docs_build`, `docs_clean`,
and `api_docs_build` for better user feedback during execution.
- Introduced a `clean-cache` target to the `docs` `Makefile` to clear
cached dependencies and ensure clean builds.
#### Improved dependency handling:
- Modified `install-py-deps` to create a `.venv/deps_installed` marker,
preventing redundant/duplicate dependency installations and improving
efficiency.
#### Streamlined file generation and infrastructure setup:
- Added caching for the LangServe README download and parallelized
feature table generation
- Added user-friendly completion messages for targets like `copy-infra`
and `render`.
#### Documentation server updates:
- Enhanced the `start` target with messages indicating server start and
URL for local documentation viewing.
---
### Documentation Improvements:
#### Content clarity and consistency:
- Standardized section titles for consistency across documentation
files.
[[1]](diffhunk://#diff-9b1a85ea8a9dcf79f58246c88692cd7a36316665d7e05a69141cfdc50794c82aL1-R1)
[[2]](diffhunk://#diff-944008ad3a79d8a312183618401fcfa71da0e69c75803eff09b779fc8e03183dL1-R1)
- Refined phrasing and formatting in sections like "Dependency
management" and "Formatting and linting" for better readability.
[[1]](diffhunk://#diff-2069d4f956ab606ae6d51b191439283798adaf3a6648542c409d258131617059L6-R6)
[[2]](diffhunk://#diff-2069d4f956ab606ae6d51b191439283798adaf3a6648542c409d258131617059L84-R82)
#### Enhanced workflows:
- Updated instructions for building and viewing documentation locally,
including tips for specifying server ports and handling API reference
previews.
[[1]](diffhunk://#diff-048deddcfd44b242e5b23aed9f2e9ec73afc672244ce14df2a0a316d95840c87L60-R94)
[[2]](diffhunk://#diff-048deddcfd44b242e5b23aed9f2e9ec73afc672244ce14df2a0a316d95840c87L82-R126)
- Expanded guidance on cleaning documentation artifacts and using
linting tools effectively.
[[1]](diffhunk://#diff-048deddcfd44b242e5b23aed9f2e9ec73afc672244ce14df2a0a316d95840c87L82-R126)
[[2]](diffhunk://#diff-048deddcfd44b242e5b23aed9f2e9ec73afc672244ce14df2a0a316d95840c87L107-R142)
#### API reference documentation:
- Improved instructions for generating and formatting in-code
documentation, highlighting best practices for docstring writing.
[[1]](diffhunk://#diff-048deddcfd44b242e5b23aed9f2e9ec73afc672244ce14df2a0a316d95840c87L107-R142)
[[2]](diffhunk://#diff-048deddcfd44b242e5b23aed9f2e9ec73afc672244ce14df2a0a316d95840c87L144-R186)
---
### Minor Changes:
- Added support for a new package name (`langchain_v1`) in the API
documentation generation script.
- Fixed minor capitalization and formatting issues in documentation
files.
[[1]](diffhunk://#diff-2069d4f956ab606ae6d51b191439283798adaf3a6648542c409d258131617059L40-R40)
[[2]](diffhunk://#diff-2069d4f956ab606ae6d51b191439283798adaf3a6648542c409d258131617059L166-R160)
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
**Description:**
Fixes a bug in the file callback test where ANSI escape codes were
causing test failures. The improved test now properly handles ANSI
escape sequences by:
- Using exact string comparison instead of substring checking
- Applying the `strip_ansi` function consistently to all file contents
- Adding descriptive assertion messages
- Maintaining test coverage and backward compatibility
The changes ensure tests pass reliably even when terminal control
sequences are present in the output
**Issue:** Fixes#32150
**Dependencies:** None required - uses existing dependencies only.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
This PR addresses the common issue where users struggle to pass custom
parameters to OpenAI-compatible APIs like LM Studio, vLLM, and others.
The problem occurs when users try to use `model_kwargs` for custom
parameters, which causes API errors.
## Problem
Users attempting to pass custom parameters (like LM Studio's `ttl`
parameter) were getting errors:
```python
# ❌ This approach fails
llm = ChatOpenAI(
base_url="http://localhost:1234/v1",
model="mlx-community/QwQ-32B-4bit",
model_kwargs={"ttl": 5} # Causes TypeError: unexpected keyword argument 'ttl'
)
```
## Solution
The `extra_body` parameter is the correct way to pass custom parameters
to OpenAI-compatible APIs:
```python
# ✅ This approach works correctly
llm = ChatOpenAI(
base_url="http://localhost:1234/v1",
model="mlx-community/QwQ-32B-4bit",
extra_body={"ttl": 5} # Custom parameters go in extra_body
)
```
## Changes Made
1. **Enhanced Documentation**: Updated the `extra_body` parameter
docstring with comprehensive examples for LM Studio, vLLM, and other
providers
2. **Added Documentation Section**: Created a new "OpenAI-compatible
APIs" section in the main class docstring with practical examples
3. **Unit Tests**: Added tests to verify `extra_body` functionality
works correctly:
- `test_extra_body_parameter()`: Verifies custom parameters are included
in request payload
- `test_extra_body_with_model_kwargs()`: Ensures `extra_body` and
`model_kwargs` work together
4. **Clear Guidance**: Documented when to use `extra_body` vs
`model_kwargs`
## Examples Added
**LM Studio with TTL (auto-eviction):**
```python
ChatOpenAI(
base_url="http://localhost:1234/v1",
api_key="lm-studio",
model="mlx-community/QwQ-32B-4bit",
extra_body={"ttl": 300} # Auto-evict after 5 minutes
)
```
**vLLM with custom sampling:**
```python
ChatOpenAI(
base_url="http://localhost:8000/v1",
api_key="EMPTY",
model="meta-llama/Llama-2-7b-chat-hf",
extra_body={
"use_beam_search": True,
"best_of": 4
}
)
```
## Why This Works
- `model_kwargs` parameters are passed directly to the OpenAI client's
`create()` method, causing errors for non-standard parameters
- `extra_body` parameters are included in the HTTP request body, which
is exactly what OpenAI-compatible APIs expect for custom parameters
Fixes#32115.
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Co-authored-by: Mason Daugherty <github@mdrxy.com>
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Further clean up of namespace:
- Removed prompts (we'll re-add in a separate commit)
- Remove LocalFileStore until we can review whether all the
implementation details are necessary
- Remove message processing logic from memory (we'll figure out where to
expose it)
- Remove `Tool` primitive (should be sufficient to use `BaseTool` for
typing purposes)
- Remove utilities to create kv stores. Unclear if they've had much
usage outside MultiparentRetriever
This PR adds scaffolding for langchain 1.0 entry package.
Most contents have been removed.
Currently remaining entrypoints for:
* chat models
* embedding models
* memory -> trimming messages, filtering messages and counting tokens
[we may remove this]
* prompts -> we may remove some prompts
* storage: primarily to support cache backed embeddings, may remove the
kv store
* tools -> report tool primitives
Things to be added:
* Selected agent implementations
* Selected workflows
* Common primitives: messages, Document
* Primitives for type hinting: BaseChatModel, BaseEmbeddings
* Selected retrievers
* Selected text splitters
Things to be removed:
* Globals needs to be removed (needs an update in langchain core)
Todos:
* TBD indexing api (requires sqlalchemy which we don't want as a
dependency)
* Be explicit about public/private interfaces (e.g., likely rename
chat_models.base.py to something more internal)
* Remove dockerfiles
* Update module doc-strings and README.md
The `_dereference_refs_helper` in `langchain_core.utils.json_schema`
incorrectly handled objects with a reference and other fields.
**Issue**: #32170
# Description
We change the check so that it accepts other keys in the object.
## Summary
- Fixed redundant word "done" in SECURITY.md line 69
- Fixed grammar errors in Fireworks README.md line 77: "how it fares
compares" → "how it compares" and "in terms just" → "in terms of"
## Test plan
- [x] Verified changes improve readability and correct grammar
- [x] No functional changes, documentation only
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Claude <claude@anthropic.com>
Co-authored-by: Claude <noreply@anthropic.com>
Multiple models were
[retired](https://docs.anthropic.com/en/docs/about-claude/model-deprecations#model-status)
yesterday.
Tests remain broken until we figure out what to do with the legacy
Anthropic LLM integration— currently uses their (legacy) text
completions API, for which there appear to be no remaining supported
models.
* Adding support for more Chroma client options (`HttpClient` and
`CloundClient`). This includes adding arguments necessary for
instantiating these clients.
* Adding support for Chroma's new persisted collection configuration (we
moved index configuration into this new construct).
* Delegate `Settings` configuration to Chroma's client constructors.
## Problem
When using `ChatOllama` with `create_react_agent`, agents would
sometimes terminate prematurely with empty responses when Ollama
returned `done_reason: 'load'` responses with no content. This caused
agents to return empty `AIMessage` objects instead of actual generated
text.
```python
from langchain_ollama import ChatOllama
from langgraph.prebuilt import create_react_agent
from langchain_core.messages import HumanMessage
llm = ChatOllama(model='qwen2.5:7b', temperature=0)
agent = create_react_agent(model=llm, tools=[])
result = agent.invoke(HumanMessage('Hello'), {"configurable": {"thread_id": "1"}})
# Before fix: AIMessage(content='', response_metadata={'done_reason': 'load'})
# Expected: AIMessage with actual generated content
```
## Root Cause
The `_iterate_over_stream` and `_aiterate_over_stream` methods treated
any response with `done: True` as final, regardless of `done_reason`.
When Ollama returns `done_reason: 'load'` with empty content, it
indicates the model was loaded but no actual generation occurred - this
should not be considered a complete response.
## Solution
Modified the streaming logic to skip responses when:
- `done: True`
- `done_reason: 'load'`
- Content is empty or contains only whitespace
This ensures agents only receive actual generated content while
preserving backward compatibility for load responses that do contain
content.
## Changes
- **`_iterate_over_stream`**: Skip empty load responses instead of
yielding them
- **`_aiterate_over_stream`**: Apply same fix to async streaming
- **Tests**: Added comprehensive test cases covering all edge cases
## Testing
All scenarios now work correctly:
- ✅ Empty load responses are skipped (fixes original issue)
- ✅ Load responses with actual content are preserved (backward
compatibility)
- ✅ Normal stop responses work unchanged
- ✅ Streaming behavior preserved
- ✅ `create_react_agent` integration fixed
Fixes#31482.
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Co-authored-by: Mason Daugherty <github@mdrxy.com>
This PR addresses deprecation warnings users encounter when using
LangChain tools with Pydantic v2:
```
PydanticDeprecatedSince20: The `schema` method is deprecated; use `model_json_schema` instead.
Deprecated in Pydantic V2.0 to be removed in V3.0.
```
## Root Cause
Several LangChain components were still using the deprecated `.schema()`
method directly instead of the Pydantic v1/v2 compatible approach. While
users calling `.schema()` on returned models will still see warnings
(which is correct), LangChain's internal code should not generate these
warnings.
## Changes Made
Updated 3 files to use the standard compatibility pattern:
```python
# Before (deprecated)
schema = model.schema()
# After (compatible with both v1 and v2)
if hasattr(model, "model_json_schema"):
schema = model.model_json_schema() # Pydantic v2
else:
schema = model.schema() # Pydantic v1
```
### Files Updated:
- **`evaluation/parsing/json_schema.py`**: Fixed `_parse_json()` method
to handle Pydantic models correctly
- **`output_parsers/yaml.py`**: Fixed `get_format_instructions()` to use
compatible schema access
- **`chains/openai_functions/citation_fuzzy_match.py`**: Fixed direct
`.schema()` call on QuestionAnswer model
## Verification
✅ **Zero breaking changes** - all existing functionality preserved
✅ **No deprecation warnings** from LangChain internal code
✅ **Backward compatible** with Pydantic v1
✅ **Forward compatible** with Pydantic v2
✅ **Edge cases handled** (strings, plain objects, etc.)
## User Impact
LangChain users will no longer see deprecation warnings from internal
LangChain code. Users who directly call `.schema()` on schemas returned
by LangChain should adopt the same compatibility pattern:
```python
# User code should use this pattern
input_schema = tool.get_input_schema()
if hasattr(input_schema, "model_json_schema"):
schema_result = input_schema.model_json_schema()
else:
schema_result = input_schema.schema()
```
Fixes#31458.
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Co-authored-by: mdrxy <61371264+mdrxy@users.noreply.github.com>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
This PR fixes the PostgreSQL NUL byte issue that causes
`psycopg.DataError` when inserting documents containing `\x00` bytes
into PostgreSQL-based vector stores.
## Problem
PostgreSQL text fields cannot contain NUL (0x00) bytes. When documents
with such characters are processed by PGVector or langchain-postgres
implementations, they fail with:
```
(psycopg.DataError) PostgreSQL text fields cannot contain NUL (0x00) bytes
```
This commonly occurs when processing PDFs, documents from various
loaders, or text extracted by libraries like unstructured that may
contain embedded NUL bytes.
## Solution
Added `sanitize_for_postgres()` utility function to
`langchain_core.utils.strings` that removes or replaces NUL bytes from
text content.
### Key Features
- **Simple API**: `sanitize_for_postgres(text, replacement="")`
- **Configurable**: Replace NUL bytes with empty string (default) or
space for readability
- **Comprehensive**: Handles all problematic examples from the original
issue
- **Well-tested**: Complete unit tests with real-world examples
- **Backward compatible**: No breaking changes, purely additive
### Usage Example
```python
from langchain_core.utils import sanitize_for_postgres
from langchain_core.documents import Document
# Before: This would fail with DataError
problematic_content = "Getting\x00Started with embeddings"
# After: Clean the content before database insertion
clean_content = sanitize_for_postgres(problematic_content)
# Result: "GettingStarted with embeddings"
# Or preserve readability with spaces
readable_content = sanitize_for_postgres(problematic_content, " ")
# Result: "Getting Started with embeddings"
# Use in Document processing
doc = Document(page_content=clean_content, metadata={...})
```
### Integration Pattern
PostgreSQL vector store implementations should sanitize content before
insertion:
```python
def add_documents(self, documents: List[Document]) -> List[str]:
# Sanitize documents before insertion
sanitized_docs = []
for doc in documents:
sanitized_content = sanitize_for_postgres(doc.page_content, " ")
sanitized_doc = Document(
page_content=sanitized_content,
metadata=doc.metadata,
id=doc.id
)
sanitized_docs.append(sanitized_doc)
return self._insert_documents_to_db(sanitized_docs)
```
## Changes Made
- Added `sanitize_for_postgres()` function in
`langchain_core/utils/strings.py`
- Updated `langchain_core/utils/__init__.py` to export the new function
- Added comprehensive unit tests in
`tests/unit_tests/utils/test_strings.py`
- Validated against all examples from the original issue report
## Testing
All tests pass, including:
- Basic NUL byte removal and replacement
- Multiple consecutive NUL bytes
- Empty string handling
- Real examples from the GitHub issue
- Backward compatibility with existing string utilities
This utility enables PostgreSQL integrations in both langchain-community
and langchain-postgres packages to handle documents with NUL bytes
reliably.
Fixes#26033.
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Co-authored-by: mdrxy <61371264+mdrxy@users.noreply.github.com>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
See https://docs.astral.sh/ruff/rules/#tryceratops-try
* TRY004 (replace by TypeError) in main code is escaped with `noqa` to
not break backward compatibility. The rule is still interesting for new
code.
* TRY301 ignored at the moment. This one is quite hard to fix and I'm
not sure it's very interesting to activate it.
Co-authored-by: Mason Daugherty <mason@langchain.dev>
* **Description:** Updated `parse_result` logic to handle cases where
`self.first_tool_only` is `True` and multiple matching keys share the
same function name. Instead of returning the first match prematurely,
the method now prioritizes filtering results by the specified key to
ensure correct selection.
* **Issue:** #32100
---------
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Co-authored-by: Mason Daugherty <mason@langchain.dev>
**Description:**
This PR makes argument parsing for Ollama tool calls more robust. Some
LLMs—including Ollama—may return arguments as Python-style dictionaries
with single quotes (e.g., `{'a': 1}`), which are not valid JSON and
previously caused parsing to fail.
The updated `_parse_json_string` method in
`langchain_ollama.chat_models` now attempts standard JSON parsing and,
if that fails, falls back to `ast.literal_eval` for safe evaluation of
Python-style dictionaries. This improves interoperability with LLMs and
fixes a common usability issue for tool-based agents.
**Issue:**
Closes#30910
**Dependencies:**
None
**Tests:**
- Added new unit tests for double-quoted JSON, single-quoted dicts,
mixed quoting, and malformed/failure cases.
- All tests pass locally, including new coverage for single-quoted
inputs.
**Notes:**
- No breaking changes.
- No new dependencies introduced.
- Code is formatted and linted (`ruff format`, `ruff check`).
- If maintainers have suggestions for further improvements, I’m happy to
revise!
Thank you for maintaining LangChain! Looking forward to your feedback.
Previously, we hit an index out of range error with empty variable names
(accessing tag[0]), now we through a slightly nicer error
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Fixes#32042
## Summary
Fixes a critical bug in JSON Schema reference resolution that prevented
correctly dereferencing numeric components in JSON pointer paths,
specifically for list indices in `anyOf`, `oneOf`, and `allOf` arrays.
## Changes
- Fixed `_retrieve_ref` function in
`libs/core/langchain_core/utils/json_schema.py` to properly handle
numeric components
- Added comprehensive test function `test_dereference_refs_list_index()`
in `libs/core/tests/unit_tests/utils/test_json_schema.py`
- Resolved line length formatting issues
- Improved type checking and index validation for list and dictionary
references
## Key Improvements
- Correctly handles list index references in JSON pointer paths
- Maintains backward compatibility with existing dictionary numeric key
functionality
- Adds robust error handling for out-of-bounds and invalid indices
- Passes all test cases covering various reference scenarios
## Test Coverage
- Verified fix for `#/properties/payload/anyOf/1/properties/startDate`
reference
- Tested edge cases including out-of-bounds and negative indices
- Ensured no regression in existing reference resolution functionality
Resolves the reported issue with JSON Schema reference dereferencing for
list indices.
---------
Co-authored-by: open-swe-dev[bot] <open-swe-dev@users.noreply.github.com>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Since #29963 BaseCache and Callbacks are imported in BaseLanguageModel
so there's no need to import them and rebuild the models.
Note: fix is available since `langchain-core==0.3.39` and the current
langchain dependency on core is `>=0.3.66` so the fix will always be
there.
The `num_gpu` parameter in `OllamaEmbeddings` was not being passed to
the Ollama client in the async embedding method, causing GPU
acceleration settings to be ignored when using async operations.
## Problem
The issue was in the `aembed_documents` method where the `options`
parameter (containing `num_gpu` and other configuration) was missing:
```python
# Sync method (working correctly)
return self._client.embed(
self.model, texts, options=self._default_params, keep_alive=self.keep_alive
)["embeddings"]
# Async method (missing options parameter)
return (
await self._async_client.embed(
self.model, texts, keep_alive=self.keep_alive # ❌ No options!
)
)["embeddings"]
```
This meant that when users specified `num_gpu=4` (or any other GPU
configuration), it would work with sync calls but be ignored with async
calls.
## Solution
Added the missing `options=self._default_params` parameter to the async
embed call to match the sync version:
```python
# Fixed async method
return (
await self._async_client.embed(
self.model,
texts,
options=self._default_params, # ✅ Now includes num_gpu!
keep_alive=self.keep_alive,
)
)["embeddings"]
```
## Validation
- ✅ Added unit test to verify options are correctly passed in both sync
and async methods
- ✅ All existing tests continue to pass
- ✅ Manual testing confirms `num_gpu` parameter now works correctly
- ✅ Code passes linting and formatting checks
The fix ensures that GPU configuration works consistently across both
synchronous and asynchronous embedding operations.
Fixes#32059.
<!-- START COPILOT CODING AGENT TIPS -->
---
💡 You can make Copilot smarter by setting up custom instructions,
customizing its development environment and configuring Model Context
Protocol (MCP) servers. Learn more [Copilot coding agent
tips](https://gh.io/copilot-coding-agent-tips) in the docs.
---------
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: mdrxy <61371264+mdrxy@users.noreply.github.com>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Description
The Perplexity chat model already returns a search_results field, but
LangChain dropped it when mapping Perplexity responses to
additional_kwargs.
This patch adds "search_results" to the allowed attribute lists in both
_stream and _generate, so downstream code can access it just like
images, citations, or related_questions.
Dependencies
None. The change is purely internal; no new imports or optional
dependencies required.
https://community.perplexity.ai/t/new-feature-search-results-field-with-richer-metadata/398
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
## Description
When ChatDeepSeek invokes a tool that returns a list, it results in an
openai.UnprocessableEntityError due to a failure in deserializing the
JSON body.
The root of the problem is that ChatDeepSeek uses BaseChatOpenAI
internally, but the APIs are not identical: OpenAI v1/chat/completions
accepts arrays as tool results, but Deepseek API does not.
As a solution added `_get_request_payload` method to ChatDeepSeek, which
inherits the behavior from BaseChatOpenAI but adds a step to stringify
tool message content in case the content is an array. I also add a unit
test for this.
From the linked issue you can find the full reproducible example the
reporter of the issue provided. After the changes it works as expected.
Source: [Deepseek
docs](https://api-docs.deepseek.com/api/create-chat-completion/)
Source: [OpenAI
docs](https://platform.openai.com/docs/api-reference/chat/create)
## Issue
Fixes#31394
## Dependencies:
No new dependencies.
## Twitter handle:
Don't have one.
---------
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Co-authored-by: Mason Daugherty <mason@langchain.dev>
- **Description:** Ensure that the tool description is an empty string
when creating a Structured Tool from a Pydantic class in case no
description is provided
- **Issue:** Fixes#31606
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
- **Description**: issues a warning if inf and nan are passed as inputs
to langchain_core.vectorstores.utils._cosine_similarity
- **Issue**: Fixes#31496
- **Dependencies**: no external dependencies added, only warnings module
imported
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
## Description
Currently when deserializing objects that contain non-deserializable
values, we throw an error. However, there are cases (e.g. proxies that
return response fields containing extra fields like Python datetimes),
where these values are not important and we just want to drop them.
Twitter handle: @hacubu
---------
Co-authored-by: Mason Daugherty <github@mdrxy.com>
